BRIDGE: benchmarking large language models for understanding real-world clinical practice texts.

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Title: BRIDGE: benchmarking large language models for understanding real-world clinical practice texts.
Authors: Wu J; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA., Gu B; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA., Zhou R; Siebel School of Computing and Data Science, The Grainger College of Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA., Xie K; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA., Snyder D; Department of Otorhinolaryngology-Head and Neck Surgery, Mayo Clinic, Rochester, MN, USA.; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA., Jiang Y; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA., Carducci V; Department of Otorhinolaryngology-Head and Neck Surgery, Mayo Clinic, Rochester, MN, USA., Wyss R; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA., Desai RJ; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA., Alsentzer E; Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA., Celi LA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA.; Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA., Rodman A; Division of General Internal Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA., Schneeweiss S; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA., Chen JH; Stanford Division of Computational Medicine, Stanford University, Stanford, CA, USA.; Division of Hospital Medicine, Stanford University, Stanford, CA, USA.; Clinical Excellence Research Center, Stanford University, Stanford, CA, USA., Romero-Brufau S; Department of Otorhinolaryngology-Head and Neck Surgery, Mayo Clinic, Rochester, MN, USA.; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA., Lin KJ; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. jklin@bwh.harvard.edu., Yang J; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. jyang66@bwh.harvard.edu.; Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, MA, USA. jyang66@bwh.harvard.edu.; Broad Institute of MIT and Harvard, Cambridge, MA, USA. jyang66@bwh.harvard.edu.; Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA. jyang66@bwh.harvard.edu.
Source: Nature biomedical engineering [Nat Biomed Eng] 2026 Jun 17. Date of Electronic Publication: 2026 Jun 17.
Publication Type: Journal Article
Journal Info: Publisher: Springer Nature Country of Publication: England NLM ID: 101696896 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2157-846X (Electronic) Linking ISSN: 2157846X NLM ISO Abbreviation: Nat Biomed Eng Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2157-846X
DOI:10.1038/s41551-026-01719-2